Matrix Multiplication

Matrix Multiplication is a computational operation in linear algebra that belongs to the class of binary operations on arrays, distinguished by its specific rule of computing each element of the resulting matrix as the dot product of corresponding rows and columns from two input matrices, and serves as a foundational method in GPU computing for accelerating high-throughput parallel computations.

3.1K

Publications

197.9K

Citations

6.9K

Authors

1.5K

Institutions

Publications per year

2017–2026

927

Authors

6.9K

  1. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    44 publications · 6.3K citations

    28

    h-index

  2. JD
    James Demmel

    University of California, Berkeley, United States

    33 publications · 3.4K citations

    27

    h-index

  3. VK
    Viktor K. Prasanna

    University of Southern California, United States

    26 publications · 1.5K citations

    21

    h-index

  4. RA
    Robert A. Geijn

    The University of Texas at Austin, United States

    23 publications · 2.5K citations

    19

    h-index

  5. PS
    P. Sadayappan

    The Ohio State University, United States

    29 publications · 1.3K citations

    18

    h-index

1–5 of 6.9K

Venues

  1. IEEE Transactions on Computers

    79 publications · 5.9K citations

    40

    h-index

  2. arXiv (Cornell University)

    Repository · 122 publications · 6.7K citations

    31

    h-index

  3. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 58 publications · 3.4K citations

    30

    h-index

  4. SIAM Journal on Computing

    33 publications · 4.3K citations

    29

    h-index

  5. ACM Transactions on Mathematical Software

    TOMS · 43 publications · 6.6K citations

    27

    h-index

1–5